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  • This paper presents two case studies illustrating the problem of data pre-processing as the first step in computer-aided analysis of biological signals used in clinical decision support. Methods for data extraction from ECG and EEG signals are described. We show the differences between these two signal types and the reasons why different transforms are used for their pre-processing. Analysis of ECG records is performed by the wavelet transform, and analysis of EEG records is performed by the Fourier transform. The wavelet transform allows good localisation of QRS complexes, P and T waves in time and amplitude. The average accuracy of detection of all events is above 87 per cent. Adaptive segmentation abstracts the EEG signal data into stationary segments and the Fourier transform calculates their basic characteristics. In both cases extracted data are used as inputs for learning methods.
  • This paper presents two case studies illustrating the problem of data pre-processing as the first step in computer-aided analysis of biological signals used in clinical decision support. Methods for data extraction from ECG and EEG signals are described. We show the differences between these two signal types and the reasons why different transforms are used for their pre-processing. Analysis of ECG records is performed by the wavelet transform, and analysis of EEG records is performed by the Fourier transform. The wavelet transform allows good localisation of QRS complexes, P and T waves in time and amplitude. The average accuracy of detection of all events is above 87 per cent. Adaptive segmentation abstracts the EEG signal data into stationary segments and the Fourier transform calculates their basic characteristics. In both cases extracted data are used as inputs for learning methods. (en)
Title
  • Biological Data Preprocessing: A Case Study
  • Biological Data Preprocessing: A Case Study (en)
skos:prefLabel
  • Biological Data Preprocessing: A Case Study
  • Biological Data Preprocessing: A Case Study (en)
skos:notation
  • RIV/68407700:21220/03:03087919!RIV/2004/MSM/212204/N
http://linked.open.../vavai/riv/strany
  • 77 ; 99
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • Z(MSM 210000012)
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
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http://linked.open...iv/duvernostUdaju
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http://linked.open...dnocenehoVysledku
  • 599863
http://linked.open...ai/riv/idVysledku
  • RIV/68407700:21220/03:03087919
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • ECG, EEG signal analysis (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [07E2830C3397]
http://linked.open...v/mistoKonaniAkce
  • Praha
http://linked.open...i/riv/mistoVydani
  • Praha
http://linked.open...i/riv/nazevZdroje
  • Intelligent and Adaptive Systems in Medicine
http://linked.open...in/vavai/riv/obor
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http://linked.open...cetTvurcuVysledku
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Lhotská, Lenka
  • Macek, Jan
  • Fejtová, Marcela
  • Novák, Daniel
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
http://linked.open...n/vavai/riv/zamer
number of pages
http://purl.org/ne...btex#hasPublisher
  • České vysoké učení technické v Praze. Fakulta elektrotechnická
http://localhost/t...ganizacniJednotka
  • 21220
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